Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations, such as cytometry-based events, into new variables called principal components. The transformation is defined in such a way that the first two principal components generally define the maximum variance, while each succeeding component maximizes the variance at a 90 degree rotation. Principal components defined and applied to a plot in FCS Express are accessible for analysis as new parameters.
Principle Components may allow you to find and analyze new populations of interest base on transformations of existing parameters, or even allow you to use fewer parameters to analyze the same data set. Notice in the image below that new PCA Parameters may be appended to an initial data set (left) or a new PCA only data set may be created (right).
